Abstract: A distributed computing system is configured to compute operational data for a video advertisement delivery system. Cloud-based resource are used to calculate operational parameters such as geographical data, unique advertisement delivery instances and segments of consumers that received the video advertisements.

Abstract: Exemplary embodiments provide a multi-party digital check (MP-DC) through an electronic payment system that captures payor payment data/metadata regarding the intended multiple payees. The current embodiment shows two distinct payees, however the system is scalable to more than two distinct payees. MP-DC could have originated from a paper check as a facsimile picture or could be computer generated. The MP-DC is delivered to both payees via an online channel such as email, phone text message, in app message or any other form of online communication. In the current embodiment either Payee can endorse the MP-DC completely online with it's online signature or equivalent unique key i.e. without printing out the check. Once a payee has endorsed the MP-DC the other payee gets an online notification presenting the second payee with a MP-DC. The second payee has the option to cash the MP-DC using one of the methods/options presented.

Abstract: An accelerator for bitonic sorting includes a plurality of compare-exchange circuits and a first-in, first-out (FIFO) buffer associated with each of the compare-exchange circuits. An output of each FIFO buffer is a FIFO value. The compare-exchange circuits are configured to, in a first mode, store a previous value from a previous compare-exchange circuit or a memory to its associated FIFO buffer and pass a FIFO value from its associated FIFO buffer to a subsequent compare-exchange circuit or the memory; in a second mode, compare the previous value to the FIFO value, store the greater value to its associated FIFO buffer, and pass the lesser value to the subsequent compare-exchange circuit or the memory; and in a third mode, compare the previous value to the FIFO value, store the lesser value to its associated FIFO buffer, and pass the greater value to the subsequent compare-exchange circuit or the memory.

Abstract: Provided is a computer-implemented method for inter-sententially determining a semantic relationship between a first entity and a second entity in a natural language document, comprising at least the steps of: generating a first dependency parse tree, DPT, for a first origin sentence of the document which comprises the first entity, wherein each DPT comprises at least a root node; generating a second DPT for a second origin sentence of the document which mentions the second entity; linking the root nodes of the first DPT and the second DPT so as to create a chain of words, COW; determining for each word in the COW a subtree; generating for each word in the COW a subtree embedding vector cw which is based at least on word embedding vectors xw of the words of the subtree; generating a representation vector pw for each word in the COW; and classifying, using a recurrent neural network, the semantic relationship between the first entity and the second entity, based on the input representation vectors pw.

Abstract: The present invention relates to a computer-implemented method of Neural Topic Modelling (NTM), a respective computer program, computer-readable medium and data processing system. Global-View Transfer (GVT) or Multi-View Transfer (MTV, GVT and Local-View Transfer (LVT) jointly applied), with or without Multi-Source Transfer (MST) are utilised in the method of NTM. For GVT a pre-trained topic Knowledge Base (KB) of latent topic features is prepared and knowledge is transferred to a target by GVT via learning meaningful latent topic features guided by relevant latent topic features of the topic KB. This is effected by extending a loss function and minimising the extended loss function. For MVT additionally a pre-trained word embeddings KB of word embeddings is prepared and knowledge is transferred to the target by LVT via learning meaningful word embeddings guided by relevant word embeddings of the word embeddings KB. This is effected by extending a term for calculating pre-activations.

Abstract: The present invention relates to new anti-angiopoietin 2 (ANGPT2) neutralizing antibodies for therapeutic and diagnostic methods and composition using them.

Abstract: The disclosure relates to compounds specific for IL23A and BAFF, compositions comprising the compounds, and methods of use thereof. Nucleic acids, cells, and methods of production related to the compounds and compositions are also disclosed.

Abstract: A system includes a hardware accelerator configured to perform a two-dimensional (2D) fast Fourier transform (FFT) on an M×N element array. The hardware accelerator has log2 M×N pipeline stages including an initial group of log2 M stages and a final group of log2 N stages. Each stage includes a butterfly unit, a FIFO buffer coupled to the butterfly unit, and a multiplier coupled to the butterfly unit and to an associated twiddle factor table. The hardware accelerator also includes butterfly control logic to provide elements of the M×N element array to the initial group of stages in an N direction of the array, and twiddle factor addressing logic to, for the twiddle factor tables of the initial group of stages, apply an indexed entry of the twiddle factor table to the associated multiplier. The indexed entry begins as a first entry and advances by N entries after every N cycles.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words (v1, v2, . . . vN) representing a first sequence of words in a natural language of a first text and generating an output sequence of output words (, , . . . ) representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of language structures using a deep contextualized Long-Short-Term Memory model.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words representing a first sequence of words in a natural language of a first text and generating an output sequence of output words representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of full contextual information around each word vi, wherein both preceding words v<i and following words v>i around each word vi are captured by using a bi-directional language modelling and a feed-forward fashion, wherein position dependent forward hidden layers {right arrow over (h)}i and backward hidden layers for each word vi are computed.

Abstract: A method for determining a circle of trust (CoT) includes receiving a request for the CoT, generating the CoT for the context account by: identifying a primary graph with nodes based on at least one action within a social network for the context account. The method further includes performing random walks through the nodes of the primary graph, each of the random walks including two steps, ranking each of the nodes based on an amount of the random walks that end on each of the nodes, with the CoT including a number of the highest ranking plurality of nodes, filtering content items using the CoT to identify a subset of relevant items, and providing the subset for display on a client device.

Abstract: A method and system for automatically performing a discovery of topics within temporal ordered text document collections are provided. The method includes generating a bag of words vector for each text document collection using a predefined dictionary. The method also includes iteratively calculating, based on the generated bag of words vectors, for each text document collection, a hidden topic vector representing topics of the respective text document collection using a calculated hidden state vector memorizing a hidden state of all previous text document collections.

Abstract: The disclosed relates to an analyzing of a neural network, in particular an already trained neural network, for example a recurrent neural network. For this purpose, input data to the neural network are stepwise increased. After each step, the probability for a specific classification of the neural network is analyzed. Upon the probability for the specific classification raises a predetermined threshold value, the data element which has been added last to the input data is identified to be relevant for the respective decision.

Abstract: A Semantic Textual Similarity System comprising a first Long Short Term Memory, LSTM, branch adapted to be operative, to determine text similarity, on a first text corpus, the first text corpus comprising a plurality of first text elements; wherein each first text element has a first number of distinct subdivisions. The system also comprises a second LSTM branch adapted to be operative, to determine text similarity, on a second text corpus, the second text corpus comprising a plurality of second text elements, wherein each second text element has a second number of distinct subdivisions.

Abstract: The invention relates to a computer-implemented system and method for automatically utilizing data from a document. The method may comprise the steps of: receiving a document; automatically scanning the document to identify at least one data field in the document; proposing a document definition to a user based on the scan; receiving an acceptance or modification of the document definition from the user through the user interface; automatically extracting at least one data element and at least one data field from the document using the document definition; automatically searching an existing database for at least one data element or data field that matches the data element or data field extracted from the document; and storing a link between at least one of the extracted data element or data field and a data element or data field in the existing database.

Abstract: Provided is a ticketing system adapted to retrieve a recommendation from a knowledge database in response to a received query, the ticketing system including a processor adapted to perform semantic similarity learning in textual description pairs by calculating similarity scores for similarities between the received query and tickets stored in the knowledge database of the ticketing system, wherein each textual description pair includes a textual description of the received query and a textual description of a ticket of a plurality of tickets stored in the knowledge database of the ticketing system, wherein the ticket having the maximum similarity score is identified and a solution of the identified ticket is output as the retrieved recommendation for the received query by the ticketing system.

Abstract: An accelerator for bitonic sorting includes a plurality of compare-exchange circuits and a first-in, first-out (FIFO) buffer associated with each of the compare-exchange circuits. An output of each FIFO buffer is a FIFO value. The compare-exchange circuits are configured to, in a first mode, store a previous value from a previous compare-exchange circuit or a memory to its associated FIFO buffer and pass a FIFO value from its associated FIFO buffer to a subsequent compare-exchange circuit or the memory; in a second mode, compare the previous value to the FIFO value, store the greater value to its associated FIFO buffer, and pass the lesser value to the subsequent compare-exchange circuit or the memory; and in a third mode, compare the previous value to the FIFO value, store the lesser value to its associated FIFO buffer, and pass the greater value to the subsequent compare-exchange circuit or the memory.

Abstract: A method and system for facilitating typeahead. The method can include: identifying a set of topics and/or connected accounts; storing, based on one or more signals associated with a user account, a portion of the topics and/or connected accounts in a cache; and using the cache to provide typeahead suggestions to a client in response to a request associated with the user account.

Abstract: A device for relation extraction in a natural language sentence having n words is suggested, the device comprising: a recurrent neural network for joint entity and relation extractions of entities and relations between the entities in the sentence, and an entity-relation table for storing entity labels for the entities and relation labels for the relations, wherein both the entity labels and the relation labels are defined as instances of binary relationships between certain words wi and wj in the sentence, with i?[1, . . . , n], and j?[1, . . . , n], wherein each of the entity labels is a first binary relationship for i=j, and wherein each of the relation labels is a second binary relationship for i?j, wherein the recurrent neural network is configured to fill the cells of the entity-relation table and includes a forward neural network and a backward neural network.

Abstract: Present invention concerns a method of relation extraction from a text corpus, the method comprising extracting instances from the text corpus based on seeds, wherein the seeds include at least one set of template seeds and at least one set of entity seeds. The invention also pertains to related devices and methods.